Linked Life Data

15376585

Source:http://linkedlifedata.com/resource/pubmed/id/15376585

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2004-9-20
pubmed:abstractText
In image compression context-based entropy coding is commonly used. A critical issue to the performance of context-based image coding is how to resolve the conflict of a desire for large templates to model high-order statistic dependency of the pixels and the problem of context dilution due to insufficient sample statistics of a given input image. We consider the problem of finding the optimal quantizer Q that quantizes the K-dimensional causal context Ct = (Xt-t1,Xt-t2,...,X t-tK) of a source symbol Xt into one of a set of conditioning states. The optimality of context quantization is defined to be the minimum static or minimum adaptive code length of given a data set. For a binary source alphabet an optimal context quantizer can be computed exactly by a fast dynamic programming algorithm. Faster approximation solutions are also proposed. In case of m-ary source alphabet a random variable can be decomposed into a sequence of binary decisions, each of which is coded using optimal context quantization designed for the corresponding binary random variable. This optimized coding scheme is applied to digital maps and alpha-plane sequences. The proposed optimal context quantization technique can also be used to establish a lower bound on the achievable code length, and hence is a useful tool to evaluate the performance of existing heuristic context quantizers.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1057-7149
pubmed:author
pubmed:issnType
Print
pubmed:volume
13
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
509-17
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Optimal context quantization in lossless compression of image data sequences.
pubmed:affiliation
Research Center COM, Technical University of Denmark, DK-2800 Lyngby, Denmark. sf@com.dtu.dk
pubmed:publicationType
Journal Article, Comparative Study, Evaluation Studies, Validation Studies